# On the limitations of data-based price discrimination

arXiv: https://arxiv.org/abs/2204.12723 · HTML: https://mlsanity.com/paper/2204.12723/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 27 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [The Sample Complexity of Revenue Maximization](https://mlsanity.com/paper/1502.00963/) (2015)
- [Settling the Sample Complexity of Single-parameter Revenue Maximization](https://mlsanity.com/paper/1904.04962/) (2019)
- [The Sample Complexity of Auctions with Side Information](https://mlsanity.com/paper/1511.02296/) (2015)
- [Making the Most of Your Samples](https://mlsanity.com/paper/1407.2479/) (2014)
- [Learning Economic Parameters from Revealed Preferences](https://mlsanity.com/paper/1407.7937/) (2014)
- [Randomization beats Second Price as a Prior-Independent Auction](https://mlsanity.com/paper/1507.08042/) (2015)
- [Optimal Pricing with a Single Point](https://mlsanity.com/paper/2103.05611/) (2021)
- [Are Two (Samples) Really Better Than One? On the Non-Asymptotic Performance of Empirical Revenue Maximization](https://mlsanity.com/paper/1802.08037/) (2018)

## Abstract (arXiv)

The classic third degree price discrimination (3PD) model requires the knowledge of the distribution of buyer valuations and the covariate to set the price conditioned on the covariate. In terms of generating revenue, the classic result shows that 3PD is at least as good as uniform pricing. What if the seller has to set a price based only on a sample of observations from the underlying distribution? Is it still obvious that the seller should engage in 3PD? This paper sheds light on these fundamental questions. In particular, the comparison of the revenue performance between 3PD and uniform pricing is ambiguous overall when prices are set based on samples. This finding is in the nature of statistical learning under uncertainty: a curse of dimensionality, but also other small sample complications.